The overall goal of this project is to develop novel statistical methods to assist in the positional identification of genetic factors influence quantitative traits. Towards this aim we propose six major aims. First, we will refine statistical methods for linkage analysis of complex traits. We propose model development for detecting effects due to gene-environment interactions and from imprinted loci. The developments we propose are applicable to any type of genetic modeling but are specifically studied in applications to variance components models. Our second aim will evaluate methods for gene localization of quantitative traits when very fine mapping is conducted. For this aim, we will evaluate methods that partition variability into sources attributable to linkage transmission disequilibrium tests with unconditional tests (ANOVA type) that incorporate genome control for varying degrees of population stratification. We will also study Bayesian transmission disequilibrium tests for quantitative traits. Our third aim develops empirical Bayes approaches to obtain more accurate and precise estimators of variance components in meta-analysis of linkage studies. Our fourth aim will study empirical Bayes methods for evaluating and characterizing intrastudy and interstudy heterogeneity. Our fifth aim will develop software to assist users in the planning and analysis of studies to identify quantitative traits. Finally, we will apply the methods that we are developing as part of existing studies of obesity, cancer-predisposition and rheumatoid arthritis.